Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulations within 1 hour. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, three case studies focusing on polarimetric variables are included. A fourth case study enhances the overall understanding of these events, emphasizing the underexplored nature of short-term intense rainfall studies in Brazil. Results reveal that the generating system significantly influences the rainfall pattern, especially in the Southern, Southeastern, and Central-Western regions. Regional CFADs unveil primary convective columns with 40-50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely specific warnings.

Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulation within 1 h. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, one case study focusing on polarimetric variables is included. The results reveal that the generating system (synoptic or mesoscale) of intense rain events significantly influences the rainfall pattern, mainly in the South, Southeast, and Midwest regions. Regional CFADs unveil primary convective columns with 40–50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely, specific warnings.

The number of natural disasters triggered by extreme events is increasing worldwide and significantly impacts modern society. Extreme rainfall is one of the most important factors contributing to these events. A better understanding of the physical process that causes extreme rainfall can allow rapid responses from decision-makers to lessen the impact of natural disasters on the local population. Satellite monitoring is widely used for this purpose and is essential for regions where terrestrial observations are limited or non-existent. The primary purpose of this study is to describe the performance of satellite products for extreme rainfall events that caused natural disasters in various climate regimes in Brazil and discuss the contribution of mesoscale convective systems (MCS) to these events. We defined regions based on the climatological rainfall distribution. Cases with rain values above the 99th percentile during 2012–2016 were considered statistically extreme. Our analysis is based on three datasets, with precipitation from (i) rain gauge stations, (ii) different satellite-based estimates, and (iii) mesoscale convective tracking data. The methodology was based on identifying extreme rainfall events, analyzing the performance of satellite precipitation estimates and, finally, quantifying the influence of convective systems on extreme rain. Although all regions of Brazil may be affected by natural disasters caused by extreme rains, the results suggest that the impacts caused in each region are different in magnitude. Convective systems explained over 90% of extreme rains in the case analyzed in Brazil’s south and about 60% to 90% of extreme rains in the case analyzed in the Northeast. In general, satellite products have identified rain events; however, in the southern region of Brazil, products have tended to overestimate rainfall, while other regions have tended to underestimate extreme rain values. The methods used in satellite precipitation estimation products have limitations to accurately identifying specific extreme rain events.

Observations from geostationary satellites can provide spatially continuous coverage at continental scales with high spatial and temporal resolution. Because of this, they are commonly used to complement ground-based precipitation measurements, whose coverage is often more limited. We present Hydronn, a neural-network-based, near-real-time precipitation retrieval for Brazil based on visible and infrared (Vis–IR) observations from the Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite 16 (GOES-16). The retrieval, which employs a convolutional neural network to perform Bayesian precipitation retrievals, was developed with the aims of (1) leveraging the full potential of latest-generation geostationary observations and (2) providing probabilistic precipitation estimates with well-calibrated uncertainties. The retrieval is trained using more than 3 years of collocations with combined radar and radiometer retrievals from the Global Precipitation Measurement (GPM) core observatory over South America. The accuracy of instantaneous precipitation estimates is assessed using a separate year of GPM combined retrievals and compared to retrievals from passive microwave (PMW) sensors and HYDRO, the Vis–IR retrieval that is currently in operational use at the Brazilian Institute for Space Research. Using all available channels of the ABI, Hydronn achieves accuracy close to that of state-of-the-art PMW precipitation retrievals in both precipitation estimation and detection despite the lower information content of the Vis–IR observations. Hourly, daily, and monthly precipitation accumulations are evaluated against gauge measurements for June and December 2020 and compared to HYDRO, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), and the Integrated Multi-satellitE Retrievals for GPM (IMERG). Compared to HYDRO, Hydronn reduces the mean absolute error for hourly accumulations by 21 % (22 %) compared to HYDRO by 44 % (41 %) for the mean squared error (MSE) and increases the correlation by 138 % (312 %) for June (December) 2020. Compared to IMERG, the improvements correspond to 16 % (14 %), 12 % (12 %), and 20 % (56 %), respectively. Furthermore, we show that the probabilistic retrieval is well calibrated against gauge measurements when differences in the distributions of the training data and the gauge measurements are accounted for. Hydronn has the potential to significantly improve near-real-time precipitation retrievals over Brazil. Furthermore, our results show that precipitation retrievals based on convolutional neural networks (CNNs) that leverage the full range of available observations from latest-generation geostationary satellites can provide instantaneous precipitation estimates with accuracy close to that of state-of-the-art PMW retrievals. The high temporal resolution of the geostationary observation allows Hydronn to provide more accurate precipitation accumulations than any of the tested conventional precipitation retrievals. Hydronn thus clearly shows the potential of deep-learning-based precipitation retrievals to improve precipitation estimates from currently available satellite imagery.

Precipitation is a critical component of Earth's hydrological cycle since the latent heat energy contained in the clouds cannot be seen or measured directly. Rainfall, however, is the product of the release of this energy, and rainfall can be measured. Due to the rapid variability and high intermittence, in space and time, of the water and energy cycle elements (rainfall, clouds, and water vapor), the satellite-based retrievals (especially those from low-inclination orbit missions) are the best way to monitor and quantify the water and energy exchanges over the tropics, since the number of available meteorological stations are not enough to cover adequately all continental tropical regions. The aim of this chapter is to summarize the most recent studies on the cloud physical process involved in rainfall occurrence in tropical continental regions, including the main results of CHUVA project—CHUVA, meaning "rain" in Portuguese—and some ground validation activities over Brazil. Some improvement on cloud microphysical description over tropical regions by using polarimetric radars and associated electrification processes will be also presented. Finally, the contribution of the aerosol in the process of formation of precipitation over tropical regions will be showcased in the last section of the chapter.

Different algorithms for forecasting and tracking meteorological systems have been developed over the years. Many of them are used to study cloud propagation, precipitation and lightning for nowcasting. Therefore, it is necessary to define carefully the parameters (e.g., intensity thresholds and minimum size) that impact tracking of these variables. In order to represent the physical aspects of rain propagation over the Amazon region, several methods of correction and displacement detection were studied. Different parameters were used to validate the methods based on the extrapolated rain cell. A probability detection of 78.4% and 68.6% was achieved for 20 dBZ thresholds during the wet and dry season, respectively. However, the POD decreases for higher reflectivity thresholds. The results for corrections by Inner Nuclei showed that embedded convection can dictate the propagation of rain cells. Split and merge corrections performed well; however, they applied only to a few cases. Corrections performed better for precipitating systems with larger areas and longer duration. The correction methods showed similar skills for both seasons. Which shows that they are able to monitor rain cells throughout the year. The automated combination of different methods for the 20 dBZ threshold proved to be the best choice for tracking rainfall in the Amazon region.

Excessive rain may cause several problems for society. Understanding the behaviour of extreme rainfall and quantifying it in an assertive manner is important for whole society. The purpose of this work is to evaluate the ability of satellite precipitation products to detect the extreme rainfall over different regions of Brazil. The products evaluated in this investigation were from Frequent Rainfall Observations on GridS (FROGS) database. The results show that, each region of Brazil is characterized by extremes of rain with different intensities. The regions that presented the highest values are south and north regions of Brazil with values around 125.0 mm/ day. In both regions, the GSMAP product (with rain gauges adjustments) have better performance, as shown in the metrics for the south and north regions where bias = -1.20 mm/day and -6.49 mm/day; r = 0.65 and 0.50; std = 10.15 mm/day and 10.63 mm/day; rmse = 9.58 mm/day and 13.16 mm/day respectively. On the other hand, the regions with the lowest intensities are the northeastern region, inland and coast, presented frequent extreme values of approximately 35.0 mm/day. At these regions, both versions of product 3B42RT v7.0 demonstrated a better performance, as demonstrated in the metrics for inland and coast northeastern regions, bias = 2.82 mm/day and -2.94 mm/day; r = 0.18 and 0.30; std = 8.53 mm/day and 6.97 mm/day; rmse = 14.75 mm/day and 7.03 mm/day, respectively. It is worth mentioning that the precipitation values found in this work do not necessarily cause disasters or generate impacts in the analyzed regions, they were considered extreme from a statistical point of view, considering the analyzed database.

The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to resolve the diurnal cycle properly. This study also proposes to evaluate different methodologies for partitioning gauge-adjusted daily precipitation products, i.e., a product made by the combination of satellite estimates and surface gauge observations, into 3-h precipitation accumulation. Two methodologies based on the calculation of a conversion factor F between a daily gauge-adjusted product, combined scheme (CoSch, hereafter), and a non-gauge-adjusted one, the integrated multi-satellite retrievals for GPM (IMERG)-Early (IMERG, hereafter) were tested for this research. Hourly rain gauge stations for the period of 2015–2018 over Brazil were used to assess the performance of the proposed methodologies over the whole region and five sub-regions with homogeneous precipitation regimes. Standard statistical metrics and categorical indices related with the capability to detect rainfall events were used to compare the ability of each product to represent the diurnal cycle. The results show that the new 3-h CoSch products show better agreement with rainfall gauge stations when compared with IMERG, better capturing the diurnal cycle of precipitation. The biggest improvement was over northeastern region close to the coast, where IMERG was not able to capture the diurnal cycle properly. One of the proposed methodologies (CoSchB) performed better on the critical success index and equitable threat score metrics, suggesting that this is the best product over the two. The downside, when compared with the other methodology (CoSchA), was a slight increase in the values of bias and mean absolute error, but still at acceptable levels.

Precipitation is an extremely important variable for society. While intense and persistent rainfall are responsible for causing floods and landslides, its absence is also a factor of concern, such as droughts. For an efficient rainfall monitoring over a certain region, sub-daily measurements of this variable are required to understand the physical processes which modulate the so-called Precipitation Diurnal Cycle (PDC). Over Brazil, due to the low density of ground observational data, both from rain gauges networks (most of them available on daily basis) and weather radars, it is necessary to use satellite-based rainfall estimation products. However, the error for those techniques on sub-daily scale are still high. In this context, this study analyzes Artificial Intelligence techniques, specifically Artificial Neural Networks (ANN), for downscaling daily to a sub-daily scale precipitation data using multiple datasets. The main information from daily retrievals comes from a satellites-based technique corrected by rain gauges, called MERGE which was developed by INPE in Brazil. MERGE has an available dataset of 20 years. In order to better represent the characteristics of the diurnal cycle and the physical processes of the different regions of the country we applied two different types of ANN, the Deep Neural Network (DNN) and the Recurrent Neural Network (RNN). The target is a sub-daily rainfall with temporal resolution of 3 hours. Meteorological variables with physical relationship with the rain in previous studies were selected, like infrared brightness temperature from GOES satellite, hourly precipitation estimates from microwave sensors (IMERG), and environmental data (e.g. humidity, wind, etc) from ERA reanalysis. Also, we used topography and location information for the whole area. Each of the chosen variables was pre-processed, producing averages (or accumulated) values and other 3-hour temporal resolution measurements. Correlation between them and the accumulated observed rain at the same time were analyzed. The results were evaluated for different regions, seasons, and times. Results obtained by the ANNs are in a better agreement when compared to IMERG product (the reference). For results with less input data (e.g. without wind information), to save computer time, the DNN has the best performance, especially when trained with data from all regions. DNN obtained an MSE of 11.09 mm and RNN shows a value of 11.88 mm. However, the rain screening (areas with rainfall) is slightly better for IMERG, but with a superestimation of the precipitation. Also, DNN shows better results for all the different regions of Brazil as well as for the different seasons. BIAS for RNN is better for hours with low precipitation, while DNN and IMERG are better for rainy periods (18 and 21 GMT). However, BIAS differences between DNN and RNN are very small and MSE shows a slightly better values to DNN for all times. Therefore, DNN was chosen as the best ANN. Sensitivity tests will be carried out to determine the best DNN configuration without considering computational costs. For its improved version, with the inclusion of more meteorological variables, DNN performed better in all aspects, including rain screening, when compared to IMERG.

Knowing the spatiotemporal distribution of precipitation is undoubtedly important for planning various economic/social activities, such as agriculture, livestock, and energy production. The coarse observation density over certain regions may significantly compromise the quality of precipitation products interpolated by only surface observations. To minimize the lack of observations over certain regions, the Centre for Weather Forecast and Climate Studies (CPTEC) of National Institute for Space Research (INPE) developed two types of blended precipitation products, namely, the Combined Scheme (CoSch) and MERGE, which combine observed precipitation data with satellite estimates on a daily scale. To understand how different blending methodologies impact the final results, a comparison of each algorithm with independent rain gauges was performed with a focus over the Brazilian territory. Both products were generated at a 10-km horizontal resolution using input data from the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG-Early) for product (Version 5) in conjunction with surface observations from Surface Synoptic Observations (SYNOP), data collection platforms (DCPs) and data from regional meteorological centres. The cumulative 24-hour precipitation was evaluated for the period from June 2014 to June 2017. The results show that both products reliably characterize the precipitation regimes over most of the study regions, although MERGE and CoSch tend to over- and underestimate the amount of precipitation, respectively. However, the magnitude of the Bias achieved by MERGE is smaller than that achieved by CoSch. Overall, MERGE outperforms CoSch when analysing rain/no rain and light to moderate rainfall (0.5 to 20.0 mm). For heavy precipitation (>35.0 mm), the performance of both products is similar. The most significant differences between the two products occur over the Northeast Region of Brazil (R3 and R4), where CoSch tends to encounter difficulties characterizing the precipitation regime during the northeastern wet period (April - November). In R3 and R4, MERGE relies more on surface observations, whereas CoSch relies on GPM-IMERG-Early, which could be associated with the deficiency of GPM-IMERG-Early in estimating the amount of precipitation associated with warm clouds.

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil’s National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes.

Thunderstorms in southeastern South America (SESA) stand out in satellite observations as being among the strongest on Earth in terms of satellite-based convective proxies, such as lightning flash rate per storm, the prevalence for extremely tall, wide convective cores and broad stratiform regions. Accurately quantifying when and where strong convection is initiated presents great interest in operational forecasting and convective system process studies due to the relationship between convective storms and severe weather phenomena. This paper generates a novel methodology to determine convective initiation (CI) signatures associated with extreme convective systems, including extreme events. Based on the well-established area-overlapping technique, an adaptive brightness temperature threshold for identification and backward tracking with infrared data is introduced in order to better identify areas of deep convection associated with and embedded within larger cloud clusters. This is particularly important over SESA because ground-based weather radar observations are currently limited to particular areas. Extreme rain precipitation features (ERPFs) from Tropical Rainfall Measurement Mission are examined to quantify the full satellite-observed life cycle of extreme convective events, although this technique allows examination of other intense convection proxies such as the identification of overshooting tops. CI annual and diurnal cycles are analyzed and distinctive behaviors are observed for different regions over SESA. It is found that near principal mountain barriers, a bimodal diurnal CI distribution is observed denoting the existence of multiple CI triggers, while convective initiation over flat terrain has a maximum frequency in the afternoon.

A new data set was commissioned over Latin America with the goal of supporting decision‐making in various socioeconomic activities and, in particular, for climate insurance products. The Historical Database for Gridded Daily Precipitation Dataset over Latin America ( LatAmPrec ), based on the combined scheme approach developed at the Centro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais (CPTEC/INPE), provides a new high‐resolution, low‐latency, gauge–satellite‐based analysis of daily precipitation over Latin America for the period March 2000–July 2017. In order to understand the strengths and limitations of the new data set for use in weather index insurance, the present study applies two different validation methodologies. The first focuses on capturing, through a cross‐correlation process, the accuracy and improved characteristics of the new gauge‐merged data set. Second, to gauge the skill of the data set in the context of insurance losses, the study uses a statistical approach, previously applied at a village level and here applied at regional levels, to assess how well the new data set predicts evidence of loss events. This is performed for both farmer interview data and national‐level disaster data sets. The results from both validation methodologies show that LatAmPrec performs well when compared with other data sources and can satisfactorily capture the insurance‐relevant losses on the ground. One main advantage of the new product is its high spatial resolution and low latency compared with other existing products used in the weather index insurance industry.

Rainfall measurement is a very important topic to society and for the understanding of the weather and climate, therefore needs to be calculated as accurately as possible. Counteracting the problem of the high temporal and spatial variability of precipitation, geostationary satellites sensors have been proved an excellent tool to this task, providing scans with high temporal resolution and detecting the growth and decay of rain cells. Using infra-red (IR) images obtained from the Geostationary Operational Environmental Satellites (GOES), the Hydro-Estimator (HYDRO) algorithm produces instantaneous precipitation estimates with 30 min temporal resolution and 4 km spatial resolution with a very low latency compared with other more sophisticated methodologies (i.e. passive microwave-based algorithms). However, the IR algorithm has some limitations to estimate precipitation on some cloud systems. In order to overcome this problem, the main objective of this study is to develop a light and fast algorithm, based on the histogram matching (HM) technique, to combine the superior sampling and low latency of the HYDRO IR product with more accurate active microwave-based products over Brazil. The adjusted HYDRO (AHYDRO) product was validated against Brazil rain gauge network for two years (2016-2017) and the performance was assessed by using standard statistical metrics and categorical indices. Results show that the HM technique is able to minimize the large variability and discrepancies among HYDRO and observed precipitation over Brazil. At same time, is able to generate a better bias performance while maintaining the same correlation levels before the adjustment.